An intelligent agent, such as a meeting scheduling system, has a
set of constraints which characterizes what that agent can do.
However, a dynamic environment may require that a system alter its
constraints. If situation-specific feedback is available, a
system may be able to adapt by reflecting on its own reasoning
processes. Such reflection may be guided not only by explicit
representation of the system's constraints but also by explicit
representation of the functional role that those constraints play
in the reasoning process. We present an operational computer
program, SIRRINE2 which uses Task-Method-Knowledge models of a
system to reason about traits such as system constraints. We
further describe an experiment with SIRRINE2 in the domain of
meeting scheduling.

Analyzing Political Decision Making from an Information Processing Perspective: JESSE,
Donald Sylvan, Ashok Goel, and B. Chandrasekaran.
American Journal of Political Science, 34(1):74-123, 1990.
mailto:goel@cc.gatech.edu

AskJef is a prototype AI system that helps software
engineers in designing human-machine interfaces. It provides a
memory of interface design examples, primitive domain objects, and
design principles, guidelines, errors and stories. The design
examples are represented graphically and decomposed
temporally. The different types of knowledge are cross-indexed to
enable the designer to navigate through the system's
memory. AskJef helps software engineers in (1) understanding
interface design problems by illustrating and explaining solutions
to similar examples, and (2) comprehending the domain of interface
design by illustrating and explaining the use of design
guidelines. It uses text, graphics, animation and voice to present
relevant information to the designer.

Analogical reasoning appears to play a key role in creative
design. This article provides a brief overview of recent AI research
on analogy-based creative design. It begins with an examination of
characterizations of creative design. Then it analyzes theories of
analogical design in terms of four questions: why, what, how, and
when. Next it briefly describes three recent AI theories of
analogy-based creative design: SYN [Borner et al 1996], DSSUA [Qian
and Gero 1992], and IDEAL [Bhatta 1995]. Finally it enumerates a set
of research issues in analogy-based creative design.

Explanation is an important issue in building computer-based
interactive design environments in which a human designer and a
knowledge system may cooperatively solve a design problem. We
consider the two related problems of explaining the system's
reasoning and the design generated by the system. In particular,
we analyze the content of explanations of design reasoning and
design solutions in the domain of physical devices. We describe
two complementary languages: task-method-knowledge models for
explaining design reasoning, and structure-behavior-function
models for explaining device designs. Interactive Kritik is a
computer program that uses these representations to visually
illustrate the system's reasoning and the result of a design
episode. The explanation of design reasoning in Interactive Kritik
is in the context of the evolving design solution, and, similarly,
the explanation of the design solution is in the context of the
design reasoning.

Generality and scale are important but difficult issues in
knowledge engineering. At the root of the difficulty lie two hard
questions: how to accumulate huge volumes of knowledge, and how to
support heterogeneous knowledge and processing? One answer to the
first question is to reuse legacy knowledge systems, integrate
knowledge systems with legacy databases, and enable sharing of the
databases by multiple knowledge systems. We present an architecture
called HIPED for realizing this answer. HIPED converts the second
question above into a new form: how to convert data accessed from a
legacy database into a form appropriate to the processing method used
in a legacy knowledge system? One answer to this reformed question is
to use method-specific transformation of data into knowledge. We
describe an experiment in which a legacy knowledge system called
Interactive Kritik is integrated with an ORACLE database using IDI as
the communication tool. The experiment indicates the computational
feasibility of method-specific data-to-knowledge transformations.

From Design Cases to Generic Mechanisms,
Sambasiva Bhatta and Ashok Goel.
To appear in Artificial Intelligence in Engineering Design,Analysis and Manufacturing, Special Issue on Machine Learning, Vol. 10, in press..
mailto:goel@cc.gatech.edu

A Functional Approach to Program Understanding,
Eleni Stroulia and Ashok Goel.
Proc. AAAI-92 workshop on AI and Automated Program Understanding, San Jose, July 1992, pp. 120-124.
mailto:goel@cc.gatech.edu

A key step in explaining how something works is explaining what
that thing was intended to do. This is equally true of physical
devices and of abstract devices such as knowledge systems. In this
paper, we consider the problem of providing functionally oriented
explanations of a knowledge-based design system. In particular, we
analyze the content of explanations of reasoning in the context of the
design of physical devices. We describe a language for expressing
explanations: task-method-knowledge models. Additionally, we describe
the Interactive Kritik system, a computer program that makes use of
these representations to visually illustrate the system's reasoning.

Functional Models and Model-Based Diagnosis in Adaptive Design,
Ashok Goel and Eleni Stroulia.
To appear in Artificial Intelligence for Engineering Design,Analysis and Manufacturing, Special Issue on Functional Representation and Reasoning, 1996.
mailto:goel@cc.gatech.edu

Functional models have been extensively investigated in the
context of several problem-solving tasks such as device diagnosis
and design. In this paper, we view problem solvers themselves as
devices, and use structure-behavior-function models to represent
how they work. The model representing the functioning of a problem
solver explicitly specifies how the knowledge and reasoning of the
problem solver result in the achievement of its goals. Then, we
employ these models for performance-driven reflective learning. We
view performance-driven learning as the task of redesigning the
knowledge and reasoning of the problem solver to improve its
performance. We use the model of the problem solver to monitor its
reasoning, assign blame when it fails, and appropriately redesign
its knowledge and reasoning. This paper focuses on the model-based
redesign of a path planner's task structure. It illustrates the
model-based reflection using examples from an operational system
called Autognostic system.

In experience-based (or case-based) reasoning, new problems are
solved by retrieving and adapting the solutions to similar
problems encountered in the past. An important issue in
experience-based reasoning is to identify different types of
knowledge and reasoning useful for different classes of
case-adaptation tasks. In this paper, we examine a class of
non-routine case-adaptation tasks that involve patterned
insertions of new elements in old solutions. We describe a
model-based method for solving this task in the context of the
design of physical devices. The method uses knowledge of generic
teleological mechanisms (GTMs) such as cascading. Old designs are
adapted to meet new functional specifications by accessing and
instantiating the appropriate GTM. The Kritik2 system evaluates
the computational feasibility and sufficiency of this method for
design adaptation.

In this article, we present our research on the integration of natural
language understanding and problem solving capabilities in the context of
the design of physical devices. We describe an experimental integrated
system called KA [Goel and Eiselt, 1991; Pittges et al, 1993] that
illustrates some of the benefits of building an integrated theory of
multiple cognitive tasks focusing on language u nderstanding and its
interaction with design problem solving. We show for example how our work
on KA imposed constraints on the target representation of natural
language understanding and how the integrated approach redefined
classical problems in language processing such as ambiguity and
underspecification in terms of the overall goals of the KA system.
Language understanding imposed constraints, in return, on the task
structure of the design problem solver.

A Knowledge-based Selection Mechanism for Strategic Control with Application in Design, Diagnosis and Planning,
William Punch, Ashok Goel and David Brown..
International Journal of Artificial Intelligence Tools, Vol. 4 (3), pp 323-348, 1996.
mailto:goel@cc.gatech.edu

Learning and problem solving are intimately related: problem
solving determines the knowledge requirements of the reasoner
which learning must fulfill, and learning enables improved
problem-solving performance. Different models of problem solving,
however, recognize different knowledge needs, and, as a result,
set up different learning tasks. Some recent models analyze
problem solving in terms of generic tasks, methods, and
subtasks. These models require the learning of problem-solving
concepts such as new tasks and new task decompositions. We view
reflection as a core process for learning these problem-solving
concepts. In this paper, we identify the learning issues raised by
the task-structure framework of problem solving. We view the
problem solver as an abstract device, and represent how it works
in terms of a structure-behavior-function model which specifies
how the knowledge and reasoning of the problem solver results in
the accomplishment of its tasks. We describe how this model
enables reflection, and how model-based reflection enables the
reasoner to adapt its task structure to produce solutions of
better quality. The Autognostic system illustrates this reflection
process.

AI research on case-based reasoning has led to the development of many
laboratory case-based systems. As we move towards introducing
these systems into work environments, explaining the processes of
case-based reasoning is becoming an increasingly important issue.
In this paper we describe the notion of a meta-case for
illustrating, explaining and justifying case-based reasoning. A
meta-case contains a trace of the processing in a problem-solving
episode, and provides an explanation of the problem-solving
decisions and a (partial) justification for the solution. The
language for representing the problem-solving trace depends on the
model of problem solving. We describe a task-method-knowledge
(TMK) model of problem-solving and describe the representation of
meta-cases in the TMK language. We illustrate this explanatory
scheme with examples from Interactive Kritik, a computer-based
design and learning environment presently under development.

Modern knowledge systems for design typically employ multiple
problem-solving methods which in turn use different kinds of
knowledge. The construction of a heterogeneous knowledge system that
can support practical design thus raises two fundamental questions:
how to accumulate huge volumes of design information, and how to
support heterogeneous design processing? Fortunately, partial answers
to both questions exist separately. Legacy databases already contain
huge amounts of general-purpose design information. In addition,
modern knowledge systems typically characterize the kinds of knowledge
needed by specific problem-solving methods quite precisely. This leads
us to hypothesize method-specific data-to-knowledge compilation as
a potential mechanism for integrating heterogeneous knowledge systems
and legacy databases for design. In this paper, first we outline a
general computational architecture called HIPED for this
integration. Then, we focus on the specific issue of how to convert
data accessed from a legacy database into a form appropriate to the
problem-solving method used in a heterogeneous knowledge system. We
describe an experiment in which a legacy knowledge system called {\ik}
is integrated with an ORACLE database using IDI as the communication
tool. The limited experiment indicates the computational feasibility
of method-specific data-to-knowledge compilation, but also raises
additional research issues.

We analyze the blame-assignment task in the context of
experience-based design and redesign of physical devices. We
identify three types of blame-assignment tasks that differ in the
types of information they take as input: the design does not
achieve a desired behavior of the device, the design results in an
undesirable behavior, a specific structural element in the design
misbehaves. We then describe a model-based approach for solving
the blame-assignment task. This approach uses
structure-behavior-function models that capture a designer's
comprehension of the way a device works in terms of causal
explanations of how its structure results in its behaviors. We
also address the issue of indexing the models in memory. We
discuss how the three types of blame-assignment tasks require
different types of indices for accessing the models. Finally we
describe the KRITIK2 system that implements and evaluates this
model-based approach to blame assignment.

Model-Based Indexing and Index Learning in Case-Based Design,
Sambasiva Bhatta and Ashok Goel.
To appear in International Journal of Engineering Applications of Artificial Intelligence,special issue on Machine Learning in Engineering.
mailto:goel@cc.gatech.edu

We hypothesize generic models to be central in conceptual change in
science. This hypothesis has its origins in two theoretical
sources. The first source, constructive modeling, derives from a
philosophical theory that synthesizes analyses of historical
conceptual changes in science with investigations of reasoning and
representation in cognitive psychology. The theory of constructive
modeling posits generic mental models as productive in conceptual
change. The second source, adaptive modeling, derives from a
computational theory of creative design. Both theories posit
situation independent domain abstractions, i.e. generic models.
Using a constructive modeling interpretation of the reasoning
exhibited in protocols collected by John Clement (1989) of a
problem solving session involving conceptual change, we employ the
resources of the theory of adaptive modeling to develop a new
computational model, ToRQUE. Here we describe a piece of our
analysis of the protocol to illustrate how our synthesis of the
two theories is being used to develop a system for articulating
and testing ToRQUE. The results of our research show how generic
modeling plays a central role in conceptual change. They also
demonstrate how such an interdisciplinary synthesis can provide
significant insights into scientific reasoning.

Spatial navigation is a classical problem in AI. In this paper, we
examine three specific hypotheses regarding multistrategy
navigation planning in visually engineered physical spaces
containing discrete pathways: (1) For hybrid robots capable of
both deliberative planning and situated action, qualitative
representations of topological knowledge are sufficient for
enabling effective spatial navigation; (2) For deliberative
planning, the case-based strategy of plan reuse generates plans
more efficiently than the model-based strategy of search without
any loss in the quality of plans or problem-solving coverage; and
(3) For the strategy of model-based search, the ``principle of
locality'' provides a productive basis for partitioning and
organizing topological knowledge. We describe the design of a
multistrategy navigation planner called Router that provides an
experimental testbed for evaluating the three hypotheses. We also
describe the embodiment of Router on a mobile robot called Stimpy
for testing the first hypothesis. Experiments with Stimpy indicate
that this hypothesis apparently is valid for hybrid robots in
visually engineered navigation spaces containing discrete pathways
such as office buildings. In addition, two different kinds of
simulation experiments with Router indicate that the second and
the third hypotheses are only partially correct. Finally, we
relate the evaluation methods and experimental designs with the
research hypotheses.

There is an increasingly large demand for software systems which
are able to operate effectively in dynamic environments. In such
environments, automated software engineering is extremely valuable
since a system needs to evolve in order to respond to changing
requirements. One way for software to evolve is for it to reflect
upon a model of its own design. A key challenge in reflective
evolution is credit assignment: given a model representing the
design elements of a complex system, how might that system
localize, identify and prioritize prospective candidates for
potential modification. We describe a model-based credit
assignment mechanism. We also report on an experiment on evolving
the design of Mosaic 2.4, an early network browser.

Knowledge-based support for learning about physical devices is a
classical problem in research on intelligent tutoring systems
(ITS). The large amount of knowledge engineering needed, however,
presents a major difficulty in constructing ITS's for learning how
devices work. Many knowledge-based design systems, on the other
hand, already contain libraries of device designs and models. This
provides an opportunity for reusing the legacy device libraries
for supporting the learning of how devices work. We report on an
experiment on the computational feasibility of this reuse of
device libraries. In particular, we describe how the
structure-behavior-function (SBF) device models in an autonomous
knowledge-based design system called Kritik enable device
explanation and exploration in an interactive design and learning
environment called Interactive Kritik.